Abstract
This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoF rel ), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoF rel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data. Copyright The Author(s) 2013
Keywords
Goodness of fitCovariancePartial least squares regressionStructural equation modelingPath coefficientStatisticsPath (computing)MathematicsPath analysis (statistics)Index (typography)EconometricsComputer science
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Publication Info
- Year
- 2012
- Type
- article
- Volume
- 28
- Issue
- 2
- Pages
- 565-580
- Citations
- 1642
- Access
- Closed
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Cite This
Jörg Henseler,
Marko Sarstedt
(2012).
Goodness-of-fit indices for partial least squares path modeling.
Computational Statistics
, 28
(2)
, 565-580.
https://doi.org/10.1007/s00180-012-0317-1
Identifiers
- DOI
- 10.1007/s00180-012-0317-1